Hugging Face enables Claude Code and Codex to directly participate in RL training.
2026.10.06 22:07:56
Beating AI Express: Hugging Face has open-sourced a Multi-harness reinforcement learning (RL) framework that enables direct integration of existing coding agents such as Claude Code, Codex, and OpenCode into RL workflows for training its open-source models, eliminating the need to build separate training environments for each agent. Large language model firms like OpenAI have long trained models within their proprietary agent environments. Hugging Face has now made this capability a general open-source tool, allowing other teams to leverage existing agents for RL directly. Models no longer need to be trained in only one agent: a single training run can cycle through Claude Code, Codex, OpenCode, and Mini-SWE-Agent to complete tasks, helping the same model adapt to different prompts, tools, and execution logic, and reducing the "specialization bias" that only works well with a single harness. The team tested the framework on a 2.6-billion-parameter small model from Liquid AI. When using identical model weights but switching agents, task pass rates dropped from approximately 62% to 33%. After training across four agent environments, the average pass rate rose from 42.2% to 54.2%, with more balanced performance improvements across different agents compared to training in a single environment.
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